> ## Documentation Index
> Fetch the complete documentation index at: https://developers.dealroom.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Filters & Sorting
> Complete reference of available filters and sort keys for each API scope.
This page is auto-generated from the API's filter registry.
Use `GET /api/reference/filters?scope={scope}` to fetch this data programmatically.
The Dealroom API supports **123 filters** across **7 scopes**.
Use the [`filter` query parameter](/mintlify/concepts/filtering) to apply filters,
and the `sort` parameter to order results.
## Companies
Scope: `companies` — Used on `/api/data/entities` when querying companies.
| Filter | Key | Type | Status | Operators | Description |
| ----------------------- | ------------------------- | ----------- | ------ | ------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
| Entity ID | `id` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Fetch specific companies by their entity id (UUID). |
| Company Name | `name` | text | ✅ | `eq` | Companies whose name contains the search term. |
| Smart Keyword | `semantic_keyword` | text | ✅ | `eq` | Rank companies by how closely they match the meaning of your phrase. |
| Founded Date | `launch_date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Year or period when the organization was launched. |
| Company Status | `company_status` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Lifecycle status. |
| HQ location | `hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Founding location | `founding_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Office location | `office_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Founding or HQ location | `founding_or_hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Growth Stage | `growth_stage` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Companies at selected maturity stages. |
| Ecosystem | `ecosystem_id` | id\_lookup | ✅ | `eq` | Companies inside a saved Dealroom ecosystem. |
| Unicorn | `is_unicorn` | boolean | ✅ | `eq` | Companies with unicorn status. |
| Hiring | `is_hiring` | boolean | ✅ | `eq` | There are open job positions for this company. |
| Has Founder | `has_founder` | boolean | ✅ | `eq` | At least one known founder linked to this company. |
| Unicorn Date | `date_became_unicorn` | date | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies that became unicorns in a chosen period. |
| Tags | `tag_id` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Companies by industry, sector, technology, business model, or other Dealroom tag. |
#### Entity ID
Example values:
* `55555555-5555-5555-5555-555555555555` — Example entity
#### Company Name
Example values:
* `Tesla`
* `Stripe`
#### Smart Keyword
Ranks companies by how closely their sector matches the meaning of your keyword, not just exact-match. Replaces sorting, and the top 1000 best matches are shown.
Example values:
* `Small modular reactors`
* `Space rockets`
* `AI language translation`
#### Founded Date
Comparison against the recorded founding date. Accepts a year (2024) or year-month (2024-01) value.
Example values:
* `2024-01`
* `2025-06`
#### Company Status
Lifecycle state recorded for each organization — whether they are operational, exited, inactive, etc.
Example values:
* `operational` — Operational
* `acquired` — Acquired
* `closed` — Closed
* `low_activity` — Low activity
#### HQ location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Office location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding or HQ location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Growth Stage
Dealroom growth-stage assignments such as early growth, late growth, and mature. Use the multi-select operators to broaden across several stages.
Example values:
* `3` — Early growth
* `4` — Late growth
* `5` — Mature
#### Ecosystem
Uses the same filters as this ecosystem's pages and landscapes.
#### Unicorn
Companies with a verified \$1B valuation or exit.
#### Hiring
Profile-level hiring signal rather than a complete jobs-market feed. Coverage depends on the company maintaining its profile.
#### Has Founder
Useful as a precondition before applying founder-specific filters, since companies without linked founders will otherwise drop out of the result silently.
#### Unicorn Date
Year when valuation of a company with Unicorn status is > \$1B
Example values:
* `2024-01`
* `2025-06`
#### Tags
The main thematic filter. Tags span industries, sub-industries, technologies, sectors, business models, impact goals, and other classifications. Use multi-value operators (`in_any`, `in_all`) to broaden or tighten across multiple tags.
Example values:
* `126403` — Fintech
| Filter | Key | Type | Status | Operators | Description |
| ------------------- | --------------------- | --------------- | ------ | -------------------------------- | -------------------------------------------- |
| Dealroom Signal | `signal_rating` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by overall Dealroom Signal rating. |
| Growth Signal | `signal_growth` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by Dealroom Signal growth score. |
| Team Signal | `signal_team` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by Dealroom Signal team score. |
| Timing Signal | `signal_timing` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by Dealroom Signal timing score. |
| Completeness Signal | `signal_completeness` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by profile completeness score. |
#### Dealroom Signal
Composite score on a 0–100 scale that blends growth, team, timing, and completeness signals. Higher values indicate stronger combined signal.
Example values:
* `70`
* `85`
#### Growth Signal
0–100 component of Dealroom Signal that looks at employee growth in the company.
Example values:
* `70`
* `85`
#### Team Signal
0–100 component of Dealroom Signal that looks at previous performance, experience, serial founders and education.
Example values:
* `70`
* `85`
#### Timing Signal
0–100 component of Dealroom Signal that measures how likely the company is to raise its next round soon.
Example values:
* `70`
* `85`
#### Completeness Signal
0–100 component of Dealroom Signal that measures how complete a company's profile is.
Example values:
* `70`
* `85`
| Filter | Key | Type | Status | Operators | Description |
| --------------------- | ----------------------- | ------------------ | ------ | -------------------------------- | ----------------------------------------------------------------- |
| Employees | `employee_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by reported headcount. |
| Valuation | `latest_valuation` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by latest known valuation. |
| Latest Valuation Date | `latest_valuation_date` | date | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies whose latest valuation was recorded in a chosen period. |
| Latest Revenue | `latest_revenue` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by latest known revenue. |
| Website Traffic | `similarweb_traffic` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by SimilarWeb monthly visits. |
#### Employees
Latest reported employee count. Use minimums, maximums, or ranges to separate lean startups, scaleups, and larger organisations.
Example values:
* `10`
* `50`
* `250`
#### Valuation
Latest valuation amount.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Latest Valuation Date
Date of the latest known valuation. Accepts year or year-month input.
Example values:
* `2024-01`
* `2025-06`
#### Latest Revenue
Latest reported revenue.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Website Traffic
Latest monthly visit count from SimilarWeb. Most useful for consumer, marketplace, or web-first companies; less meaningful for businesses with limited public web presence.
Example values:
* `10000` — 10k
* `100000` — 100k
* `1000000` — 1M
| Filter | Key | Type | Status | Operators | Description |
| ------------------ | ------------------------------ | ------- | ------ | ----------------------------- | ------------------------------------------------------------------------ |
| Promising Founder | `founder.is_promising_founder` | boolean | ✅ | `eq` | Companies with at least one founder marked as promising. |
| Super Founder | `founder.is_super_founder` | boolean | ✅ | `eq` | Companies with at least one super founder. |
| Strong Founder | `founder.is_strong_founder` | boolean | ✅ | `eq` | Companies with at least one strong founder. |
| Serial Founder | `founder.is_serial_founder` | boolean | ✅ | `eq` | Companies with at least one serial founder. |
| Gender | `founder.gender` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Companies by recorded gender of linked founders. |
| Founder University | `founder_university_name` | text | ✅ | `eq` `neq` | Companies whose founders attended a university matching the search term. |
#### Promising Founder
Early in their journey but backed by strong signals: top company or university background, managerial experience, or the right age profile.
#### Super Founder
Has previously built a company at significant scale (large funding raised or big team).
#### Strong Founder
Has meaningful prior company-building experience, just below the super-founder threshold.
#### Serial Founder
Founders who have founded more than one company in Dealroom's data.
#### Gender
Looks at gender values on linked founder records. A company is included when at least one associated founder has the selected recorded gender.
Example values:
* `male` — Male
* `female` — Female
* `non_binary` — Non-binary
* `prefer_not_to_say` — Prefer not to say
#### Founder University
Traverses founder → education links: matches any company with at least one founder who attended a university whose name contains the given substring.
Example values:
* `MIT`
* `Stanford`
| Filter | Key | Type | Status | Operators | Description |
| ------------- | --------------- | ------------------ | ------ | -------------------------------- | -------------------------------------------------------------------- |
| Total Funding | `total_funding` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Companies by total funding raised. |
| VC Backed | `is_vc_backed` | boolean | ✅ | `eq` | Companies that have raised venture capital. |
| Investor Name | `investor_name` | text | ✅ | `eq` `neq` | Companies backed by an investor whose name contains the search term. |
#### Total Funding
Total funding raised across all recorded rounds, excluding debt rounds, IPOs, ICOs, acquisitions and unverified rounds.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### VC Backed
Companies that have raised venture-capital-backed rounds: Early VC, Growth Equity VC, Late VC, Seed, Series A–I, Angel, Convertible, SPAC Private Placement.
#### Investor Name
Searches through funding round participants: matches any company that has received investment from an investor whose name contains the given substring.
Example values:
* `Sequoia`
* `Andreessen`
| Filter | Key | Type | Status | Operators | Description |
| ------------ | ----------------- | ------- | ------ | --------- | --------------------------------------------------- |
| Funded | `is_funded` | boolean | ✅ | `eq` | Companies with any recorded funding event. |
| Colt | `is_colt` | boolean | ✅ | `eq` | Companies labelled Colt. |
| Thoroughbred | `is_thoroughbred` | boolean | ✅ | `eq` | Companies labelled Thoroughbred. |
| Spinout | `is_spinout` | boolean | ✅ | `eq` | Companies labelled as spinouts. |
| Titan | `is_titan` | boolean | ✅ | `eq` | Companies labelled Titan. |
| Rising Star | `is_rising_star` | boolean | ✅ | `eq` | Companies flagged as Rising Stars. |
| Exited | `is_exited` | boolean | ✅ | `eq` | Companies that have had an exit event. |
| PE Owned | `is_pe_owned` | boolean | ✅ | `eq` | Companies currently owned by a private equity firm. |
#### Funded
Distinguishes companies with at least one financing record or investor from those with none.
#### Colt
Startups with the most recent annual revenue between $25M and $100M USD.
#### Thoroughbred
Startups with annual revenue above \$100M.
#### Spinout
Companies spun out from a university, research lab, or larger company.
#### Titan
Manually curated list of landmark companies.
#### Rising Star
Early-stage startups founded in 2020 or later with a strong Dealroom Signal score of ≥85, valued under \$1B, and still actively independent.
#### Exited
Companies recorded as exited — typically via acquisition, IPO, or merger. Use with `company_status` to further narrow by post-exit state.
#### PE Owned
Companies that have been acquired by or are majority-owned by a private equity investor.
### Sort keys
`name` · `launch_date` · `employee_count` · `employee_count_1y_growth` · `signal_rating` · `latest_valuation` · `latest_revenue` · `total_funding` · `total_invested` · `year_of_exit` · `alumni_count` · `alumni_founder_count` · `alumni_founded_companies_count` · `alumni_unicorn_companies_count` · `spinout_count` · `total_investments_count` · `founded_companies_total_funding` · `added_at` · `created_at` · `updated_at`
Prefix with `-` for descending: `sort=-name`
***
## Investors
Scope: `investors` — Used on `/api/data/investors` for investor search.
| Filter | Key | Type | Status | Operators | Description |
| ----------------------- | ------------------------- | ----------- | ------ | ------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
| Entity ID | `id` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Fetch specific investors by their entity id (UUID). |
| Investor Name | `name` | text | ✅ | `eq` | Investors whose name contains the search term. |
| HQ location | `hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Founding location | `founding_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Office location | `office_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Founding or HQ location | `founding_or_hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Selected geographies where the organization operates: cities, regions, countries, or continents. |
| Investor Type | `investor_type` | enum | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Investors by category. |
| Investor Status | `company_status` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Lifecycle status. |
| Founded Date | `launch_date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Year or period when the organization was launched. |
| Closing Date | `closing_date` | date | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Funds that closed in a chosen period. |
| Ecosystem | `ecosystem_id` | id\_lookup | ✅ | `eq` | Investors inside a saved Dealroom ecosystem. |
#### Entity ID
Example values:
* `55555555-5555-5555-5555-555555555555` — Example entity
#### Investor Name
Example values:
* `Sequoia`
* `Atomico`
#### HQ location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Office location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding or HQ location
Location IDs from the Dealroom location taxonomy. Defaults to headquarters; pair with the `location_role` parameter to filter by founding, office, or any role.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Investor Type
Investors are categorized by how they operate and deploy capital.
Example values:
* `venture_capital` — Venture capital
* `angel_fund` — Angel fund
* `private_equity` — Private equity
* `family_office` — Family office
#### Investor Status
Lifecycle state recorded for each organization. Most scouting work stays on operational; including the other states surfaces exits and inactive records.
Example values:
* `operational` — Operational
* `acquired` — Acquired
* `closed` — Closed
* `low_activity` — Low activity
#### Founded Date
Comparison against the recorded launch date. Accepts year or year-month input.
Example values:
* `2024-01`
* `2025-06`
#### Closing Date
Date the fund closed its fundraising. Accepts year or year-month input.
Example values:
* `2024-01`
* `2025-06`
#### Ecosystem
Uses the same filters as this ecosystem's pages and landscapes.
| Filter | Key | Type | Status | Operators | Description |
| ------------------- | -------------------------- | ------------------ | ------ | ------------------------------------------------ | -------------------------------------------------------------------------------------------- |
| Deal Size (Min) | `min_deal_size` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Investors by their minimum deal size. |
| Deal Size (Max) | `max_deal_size` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Investors by their maximum deal size. |
| Portfolio Size | `total_investments_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Investors by number of recorded investments. |
| Total Invested | `total_invested` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Cumulative invested amount. |
| Investment Stage | `investor_stage_id` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Investors by the company stages they prefer to back. |
| Experience | `tag_id` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Investors with experience in selected industries, sectors, technologies, or business models. |
| Preferred Round | `preferred_round` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Investors by their preferred investment round type. |
| Last Round Date | `last_investor_round_date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Investors by the date of their most recent investment round. |
| Company Invested In | `portfolio_company_id` | id\_lookup | ✅ | `eq` `neq` | Investors who have invested in a specific company. |
| LP Investments Of | `lp_investor_id` | id\_lookup | ✅ | `eq` `neq` | Investors the given investor backs as a limited partner. |
#### Deal Size (Min)
Filters by the smallest deal size recorded for the investor.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Deal Size (Max)
Filters by the largest deal size recorded for the investor.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Portfolio Size
Higher values can indicate more active or historically prolific investors, depending on data coverage.
Example values:
* `10`
* `50`
* `250`
#### Total Invested
Investors by total invested amount across their portfolio.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Investment Stage
Maps investors based on the growth stage of the companies they usually back.
Example values:
* `3` — Early growth
* `4` — Late growth
* `5` — Mature
#### Experience
Tags linked to the investor based on their portfolio and stated focus. Use for sector, industry, technology, or business-model fit when building investor shortlists.
Example values:
* `126403` — Fintech
#### Preferred Round
Filters by the investor's stated or inferred round preference — the stage at which they most commonly invest.
Example values:
* `seed` — Seed
* `series_a` — Series A
* `series_b` — Series B
#### Last Round Date
Comparison against the date of the investor's most recent portfolio round. Accepts year or year-month input. Useful for identifying active vs. inactive investors.
Example values:
* `2024`
* `2023-06`
#### Company Invested In
Returns all investors who appear as a participant in at least one funding round of the given company entity. Select a company from the autocomplete dropdown.
Example values:
* `55555555-5555-5555-5555-555555555555` — Acme Corp
#### LP Investments Of
Returns all investors that have the given investor among their known limited partners — the LP-side view of the lp\_investments graph. Select an investor from the autocomplete dropdown.
Example values:
* `55555555-5555-5555-5555-555555555555` — Acme Capital
### Sort keys
`name` · `launch_date` · `investor_rank` · `total_invested` · `total_investments_count` · `min_deal_size` · `max_deal_size` · `employee_count` · `exit_count` · `exit_total_value_usd` · `exit_pct` · `portfolio_total_valuation_usd` · `created_at` · `updated_at`
Prefix with `-` for descending: `sort=-name`
***
## Transactions
Scope: `transactions` — Used on `/api/data/transactions` for funding rounds.
| Filter | Key | Type | Status | Operators | Description |
| ----------------------- | ------------------------- | ----------- | ------ | ------------------------------------------------ | --------------------------------------------------------------------------------------------------- |
| Tags | `tag_id` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Transactions involving companies in selected industries, sectors, technologies, or business models. |
| HQ location | `hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Transactions by the related company's location. |
| Founding location | `founding_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Transactions by the related company's location. |
| Office location | `office_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Transactions by the related company's location. |
| Founding or HQ location | `founding_or_hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Transactions by the related company's location. |
| Growth Stage | `growth_stage` | id\_lookup | ✅ | `eq` `neq` `in_any` `in_all` `nin_any` `nin_all` | Transactions by the related company's maturity stage. |
| Company Name | `name` | text | ✅ | `eq` | Transactions tied to a company whose name contains the search term. |
| Investor Name | `investor_name` | text | ✅ | `eq` `neq` | Transactions involving an investor whose name contains the search term. |
| Founded Date | `launch_date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Year or period when the organization was launched. |
| EV/Revenue | `ev_revenue_multiple` | numeric | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions filtered by their enterprise-value-to-revenue multiple. |
| EV/EBITDA | `ev_ebitda_multiple` | numeric | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions filtered by their enterprise-value-to-EBITDA multiple. |
| EV/Profit | `ev_profit_multiple` | numeric | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions filtered by their enterprise-value-to-profit multiple. |
| Ecosystem | `ecosystem_id` | id\_lookup | ✅ | `eq` | Transactions whose related company belongs to a Dealroom ecosystem. |
#### Tags
Filters by tags on the related company, not the transaction record itself. Use for funding or exit activity in a specific theme.
Example values:
* `126403` — Fintech
#### HQ location
Selected geographies where the organization operates — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding location
Selected geographies where the organization operates — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Office location
Selected geographies where the organization operates — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding or HQ location
Selected geographies where the organization operates — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Growth Stage
Compares deal activity across early, breakout, late, and mature growth-stage companies.
Example values:
* `3` — Early growth
* `4` — Late growth
* `5` — Mature
#### Company Name
Example values:
* `OpenAI`
* `Stripe`
#### Investor Name
Searches through the list of investors in each round: matches any transaction where at least one participating investor's name contains the given substring.
Example values:
* `Sequoia`
* `Andreessen`
#### Founded Date
Useful for understanding whether deal activity is concentrated in newer companies or older cohorts. Accepts a year (2024) or year-month (2024-01) value.
Example values:
* `2018`
* `2024`
#### EV/Revenue
The ratio of a company's enterprise value to its revenue at the time of the transaction. Useful for benchmarking valuations across deals where revenue figures are available.
Example values:
* `3` — 3x
* `10` — 10x
#### EV/EBITDA
The ratio of enterprise value to EBITDA at the time of the transaction. A profitability-based valuation metric — useful when comparing deals where earnings data is available.
Example values:
* `3` — 3x
* `10` — 10x
#### EV/Profit
The ratio of enterprise value to profit at the time of the transaction. A useful benchmarking lens when profit is a more meaningful measure than revenue or EBITDA.
Example values:
* `3` — 3x
* `10` — 10x
#### Ecosystem
Uses the same filters as this ecosystem's pages and landscapes.
| Filter | Key | Type | Status | Operators | Description |
| ------------------ | -------------------- | ------------------ | ------ | -------------------------------- | ------------------------------------------------------------------------------------ |
| Round Type | `round_type` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Transactions by reported round type. |
| Standardized Round | `standardized_round` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | Normalized round labels, accounting for deal size, company age, and funding history. |
| Round Size | `amount` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions by round size. |
| Valuation | `valuation` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions by valuation. |
| VC Round | `is_vc_round` | boolean | ✅ | `eq` | Rounds backed by venture capital. |
| Funding Round | `is_funding_round` | boolean | ✅ | `eq` | Records that represent funding rounds. |
| Verified | `is_verified` | boolean | ✅ | `eq` | Transactions that have been verified by Dealroom. |
| Round Date | `date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Transactions in a chosen time period. |
#### Round Type
From early grants and angel rounds through Series A–I, IPOs, buyouts, debt, and acquisitions — covering the full range of funding and transaction types.
Example values:
* `SEED` — Seed
* `SERIES A` — Series A
* `SERIES B` — Series B
#### Standardized Round
Assigns a consistent round label based on the announced name, deal size, company age, and funding history. Useful when source data uses inconsistent labels.
Example values:
* `seed` — Seed
* `series_a` — Series A
* `pre_seed` — Pre-seed
* `series_b` — Series B
#### Round Size
Filter by the amount assigned to the transaction round.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Valuation
Recorded valuation on the transaction.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### VC Round
Transactions that are specifically VC rounds: Early VC, Growth Equity VC, Late VC, Seed, Series A–I, Angel, Convertible, SPAC Private Placement.
#### Funding Round
Distinguishes financings from acquisitions, mergers, and other transaction records in the same dataset.
#### Verified
Higher-confidence subset of the transaction data. Use when accuracy matters more than coverage.
#### Round Date
Comparison against the transaction date. Accepts year or year-month input.
Example values:
* `2024-01`
* `2025-06`
### Sort keys
`date` · `amount` · `round_type`
Prefix with `-` for descending: `sort=-date`
***
## People
Scope: `people` — Used on `/api/data/founders` for people/founder search.
| Filter | Key | Type | Status | Operators | Description |
| --------- | -------------- | ---------- | ------ | ----------------------------- | ------------------------------------------------ |
| Entity ID | `id` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Fetch specific people by their entity id (UUID). |
| Name | `name` | text | ✅ | `eq` | People whose name contains the search term. |
| Location | `hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | People based in selected geographies. |
| Gender | `gender` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | People by recorded gender. |
| Ecosystem | `ecosystem_id` | id\_lookup | ✅ | `eq` | People inside a Dealroom ecosystem. |
#### Entity ID
Example values:
* `55555555-5555-5555-5555-555555555555` — Example entity
#### Name
Example values:
* `Ada`
* `John`
#### Location
People based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Gender
Reflects available profile data. Coverage varies and not all people have a recorded gender value.
Example values:
* `male` — Male
* `female` — Female
* `non_binary` — Non-binary
* `prefer_not_to_say` — Prefer not to say
#### Ecosystem
Uses the same filters as this ecosystem's pages and landscapes.
| Filter | Key | Type | Status | Operators | Description |
| ----------------- | ---------------------- | ------- | ------ | --------- | ------------------------------------------------ |
| Founder | `is_founder` | boolean | ✅ | `eq` | People flagged as founders. |
| Serial Founder | `is_serial_founder` | boolean | ✅ | `eq` | Founders who have founded more than one company. |
| Promising Founder | `is_promising_founder` | boolean | ✅ | `eq` | Founders flagged as promising by Dealroom. |
| Super Founder | `is_super_founder` | boolean | ✅ | `eq` | Founders flagged as super founders by Dealroom. |
| Strong Founder | `is_strong_founder` | boolean | ✅ | `eq` | Founders flagged as strong founders by Dealroom. |
#### Founder
The main control for founder-only people lists. Use as a precondition before applying further founder-specific filters.
#### Promising Founder
Early in their journey but backed by strong signals: top company or university background, managerial experience, or the right age profile.
#### Super Founder
Has previously built a company at significant scale (large funding raised or big team).
#### Strong Founder
Has meaningful prior company-building experience, just below the super-founder threshold.
| Filter | Key | Type | Status | Operators | Description |
| ---------------- | --------------------- | ---------- | ------ | ---------- | ------------------------------------------------ |
| Work Experience | `employer_id` | id\_lookup | ✅ | `eq` `neq` | People who have worked at a specific company. |
| Current Employer | `current_employer_id` | id\_lookup | ✅ | `eq` `neq` | People currently employed at a specific company. |
#### Work Experience
Matches any person with a people\_entities link to the selected employer entity. Select a company from the autocomplete dropdown.
Example values:
* `55555555-5555-5555-5555-555555555555` — Google
#### Current Employer
Restricts to active (is\_past = false) employment records only. Select a company from the autocomplete dropdown.
Example values:
* `66666666-6666-6666-6666-666666666666` — Google
| Filter | Key | Type | Status | Operators | Description |
| -------------------------------- | --------------------------------- | ----------- | ------ | -------------------------------- | ------------------------------------------------------------------ |
| Last Founded Company Launch Date | `last_founded_entity_launch_date` | date · year | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Founders by when their most recently founded company was launched. |
#### Last Founded Company Launch Date
Comparison against the launch year of the person's last founded entity (via the lastFoundedEntityId denormalized FK). Accepts year (2020) or year-month (2020-06) input.
Example values:
* `2020`
* `2020-06`
### Sort keys
`name` · `launch_date` · `signal_rating` · `founded_companies_total_funding` · `created_at` · `updated_at`
Prefix with `-` for descending: `sort=-name`
***
## Universities
Scope: `universities` — Used on `/api/universities` for university search.
| Filter | Key | Type | Status | Operators | Description |
| ----------------------- | -------------------------------- | --------------- | ------ | -------------------------------- | -------------------------------------------------------------------- |
| Entity ID | `id` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Fetch specific universities by their entity id (UUID). |
| University Name | `name` | text | ✅ | `eq` | Universities whose name contains the search term. |
| HQ location | `hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Universities based in the selected geographies. |
| Founding location | `founding_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Universities based in the selected geographies. |
| Office location | `office_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Universities based in the selected geographies. |
| Founding or HQ location | `founding_or_hq_location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | Universities based in the selected geographies. |
| Ecosystem | `ecosystem_id` | id\_lookup | ✅ | `eq` | Universities inside a Dealroom ecosystem. |
| Alumni Count | `alumni_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Universities by total number of alumni. |
| Alumni Founders | `alumni_founder_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Universities by number of alumni who have founded companies. |
| Companies Founded | `alumni_founded_companies_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Universities by number of companies founded by their alumni. |
| Alumni Unicorns | `alumni_unicorn_companies_count` | numeric · count | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | Universities by number of unicorn companies founded by their alumni. |
#### Entity ID
Example values:
* `55555555-5555-5555-5555-555555555555` — Example entity
#### University Name
Example values:
* `Stanford`
* `Oxford`
#### HQ location
Universities based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding location
Universities based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Office location
Universities based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Founding or HQ location
Universities based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Ecosystem
Uses the same filters as this ecosystem's pages and landscapes.
#### Alumni Count
Total recorded alumni count for the university. Use range operators to find large or niche alumni networks.
Example values:
* `1000`
* `10000`
#### Alumni Founders
Number of alumni recorded as founders in Dealroom. Higher values indicate entrepreneurial output from the institution.
Example values:
* `10`
* `100`
#### Companies Founded
Total companies in Dealroom whose founders include alumni of this university.
Example values:
* `10`
* `100`
#### Alumni Unicorns
Number of \$1B+ companies whose founders include alumni of this university.
Example values:
* `1`
* `10`
### Sort keys
`name` · `launch_date` · `employee_count` · `employee_count_1y_growth` · `signal_rating` · `latest_valuation` · `latest_revenue` · `total_funding` · `total_invested` · `year_of_exit` · `alumni_count` · `alumni_founder_count` · `alumni_founded_companies_count` · `alumni_unicorn_companies_count` · `spinout_count` · `total_investments_count` · `founded_companies_total_funding` · `added_at` · `created_at` · `updated_at`
Prefix with `-` for descending: `sort=-name`
***
## News
Scope: `news` — Used on `/api/data/news` for news article search.
| Filter | Key | Type | Status | Operators | Description |
| ------------ | -------------- | ------------------ | ------ | -------------------------------- | ---------------------------------------------------------- |
| Source | `source` | text | ✅ | `eq` `neq` `in_any` `nin_any` | News article source — currently always `dealroom`. |
| Article Type | `article_type` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | News articles by type. |
| Publish Date | `publish_date` | date | ✅ | `gte` `lte` | News articles published in a chosen window. |
| Amount | `amount` | numeric · currency | ✅ | `eq` `neq` `gt` `gte` `lt` `lte` | News articles tied to a deal amount in the selected range. |
| Round Type | `round_type` | enum | ✅ | `eq` `neq` `in_any` `nin_any` | News articles mentioning a specific funding round type. |
#### Source
All next-gen news is in-house editorial, so every article's source is `dealroom`. Retained for forward-compatibility if syndicated sources return.
Example values:
* `dealroom`
#### Article Type
News articles by type — product announcements, financial milestones, funding rounds, etc.
Example values:
* `Product announcements`
* `Financial milestones`
* `Funding rounds`
#### Publish Date
News articles published in a chosen window.
Example values:
* `2024-01-01T00:00:00Z` — 2024-01-01
* `2025-01-01T00:00:00Z` — 2025-01-01
#### Amount
Mentioned deal amount extracted from the article.
Example values:
* `1000000` — 1M
* `10000000` — 10M
* `100000000` — 100M
#### Round Type
From early grants and angel rounds through Series A–I, IPOs, buyouts, debt, and acquisitions — covering the full range of funding and transaction types.
Example values:
* `SEED` — Seed
* `SERIES A` — Series A
* `SERIES B` — Series B
| Filter | Key | Type | Status | Operators | Description |
| ------------------ | ----------- | ---------- | ------ | ----------------------------- | --------------------------------------------------------------------------------------------------- |
| Mentioned Entities | `entity_id` | id\_lookup | ✅ | `eq` `in_any` | News articles that mention a specific entity. |
| Location | `location` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | News articles about entities in selected geographies. |
| Tags | `tag_id` | id\_lookup | ✅ | `eq` `neq` `in_any` `nin_any` | News articles about entities in selected industries, sectors, technologies, or other Dealroom tags. |
#### Mentioned Entities
The most direct way to retrieve articles about known Dealroom entities.
Example values:
* `345d1ab6-33df-4759-9e17-0d0c0ec9ab1c` — 10x Science
#### Location
Mentioned entities based in selected geographies — cities, regions, countries, or continents.
Example values:
* `233` — United States
* `628061` — London
* `1297711` — Berlin
#### Tags
Filter articles by the thematic tags of mentioned entities — spanning industries, sub-industries, sectors, technologies, business models, impact goals, and more. Each tag sub-type has its own picker.
Example values:
* `126403` — Fintech
### Sort keys
`publish_date` · `amount`
Prefix with `-` for descending: `sort=-publish_date`
***
## Jobs
Scope: `jobs` — Used on `/api/data/jobs` for job opening search.
| Filter | Key | Type | Status | Operators | Description |
| -------------- | ------------- | ------- | ------ | ----------------------------- | -------------------------------------------- |
| Hiring Company | `entity_id` | numeric | ✅ | `eq` `in_any` | Job openings for a specific hiring entity. |
| Source | `source` | text | ✅ | `eq` `neq` `in_any` `nin_any` | Job openings by source / provider. |
| Country | `country_id` | numeric | ✅ | `eq` `in_any` | Job openings by country location ID. |
| City | `city_id` | numeric | ✅ | `eq` `in_any` | Job openings by city location ID. |
| Region / State | `region_id` | numeric | ✅ | `eq` `in_any` | Job openings by region or state location ID. |
| Posted Date | `date_posted` | date | ✅ | `gte` `lte` | Job openings posted in a chosen window. |
#### Hiring Company
The most direct way to retrieve active openings for a known Dealroom company.
Example values:
* `1234567`
#### Source
Filters by the posting source (e.g. linkedin, predictleads).
Example values:
* `linkedin`
#### Country
Matches `jobs.country_unique_id` against a `locations.id` for a country row. Resolve IDs via `GET /api/reference/filters/location/values?q=`.
Example values:
* `2282` — Germany
#### City
Matches `jobs.city_unique_id` against a `locations.id` for a city row.
Example values:
* `118871`
#### Region / State
Matches city-region IDs via `jobs.city_region_unique_ids` or state/province IDs via `jobs.state_unique_id`.
Example values:
* `8205`
#### Posted Date
Comparison against the posting timestamp. Accepts ISO 8601 timestamps.
Example values:
* `2024-01-01T00:00:00Z` — 2024-01-01
### Sort keys
`date_posted`
Prefix with `-` for descending: `sort=-date_posted`